daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Cung Sang 2021-07-14 16:50:50 +02:00
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04-One-Dimensional-Kalman-Filters.ipynb
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@ -28,6 +28,13 @@
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"Please install SymPy before continuing. See chapter 0 for instructions.\n"
]
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"data": {
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@ -667,7 +674,7 @@
"source": [
"The result is a Gaussian that is taller than either input. This makes sense - we have incorporated information, so our variance should have been reduced. And notice how the result is far closer to the the input with the smaller variance. We have more confidence in that value, so it makes sense to weight it more heavily.\n",
"\n",
"It *seems* to work, but is it really correct? There is more to say about this, but I want to get a working filter going so you can it experience it in concrete terms. After that we will revisit Gaussian multiplication and determine why it is correct."
"It *seems* to work, but is it really correct? There is more to say about this, but I want to get a working filter going so you can experience it in concrete terms. After that we will revisit Gaussian multiplication and determine why it is correct."
]
},
{
@ -2118,7 +2125,7 @@
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@ -2132,7 +2139,7 @@
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